Reduced-state SARSA featuring extended channel reassignment for dynamic channel allocation in mobile cellular networks

This paper introduces a reinforcement learning solution to the problem of dynamic channel allocation for cellular telecommunication networks featuring either uniform or non-uniform offered traffic loads and call mobility. The performance of various dynamic channel allocation schemes are compared via extensive computer simulations, and it is shown that a reduced-state SARSA reinforcement learning algorithm can achieve superior new call and handoff blocking probabilities. A new reduced-state SARSA algorithm featuring an extended channel reassignment functionality and an initial table seeding is also demonstrated. The reduced-state SARSA incorporating the extended channel reassignment algorithm and table seeding is shown to produce superior new call and handoff blocking probabilities by way of computer simulations.